Multi-task learning is a paradigm shown to improve the performance of related tasks through their joint learning. However, for real-world data, it is usually difficult to assess the task relatedness and joint learning with unrelated tasks may lead to serious performance degradations. To this end, we propose a framework that groups the tasks based on their relatedness in a subspace and allows a varying degree of relatedness among tasks by sharing the subspace bases across the groups. This provides the flexibility of no sharing when two sets of tasks are unrelated and partial/total sharing when the tasks are related. Importantly, the number of task-groups and the subspace dimensionality are automatically inferred from the data. To realize our framework, we introduce a novel Bayesian nonparametric prior that extends the traditional hierarchical beta process prior using a Dirichlet process to permit potentially infinite number of child beta processes. We apply our model for multi-task regression and classification applications. Experimental results using several synthetic and real datasets show the superiority of our model to other recent multi-task learning methods.
|Number of pages||9|
|Publication status||Published - 1 Jan 2013|
|Event||30th International Conference on Machine Learning, ICML 2013 - Atlanta, GA, United States of America|
Duration: 16 Jun 2013 → 21 Jun 2013
|Conference||30th International Conference on Machine Learning, ICML 2013|
|Country||United States of America|
|Period||16/06/13 → 21/06/13|